To alleviate adverse environmental impacts, power stations and energy grids must optimize resource application for power generation. Accordingly, soothsaying guests' energy consumption has come integral to every energy operation system. exercising data from smart homes, energy operation information can train deep neural networks to anticipate unborn energy demands. As the frugality advances, both energy product and consumption have steadily increased over the times. Amidst global enterprises over energy force and environmental challenges, this study introduces a new vaticination approach using neural networks. By using statistical data from the energy assiduity, these networks directly read changes in energy product and consumption trends. Numerical findings validate the efficacity of this neural network- grounded vaticination system, emphasizing its significance in energy conservation sweats. Predicting energy consumption stands as a pivotal bid in energy conservation enterprise. Support vector retrogression, famed for its efficacity in handlingnon-linear data retrogression challenges, has surfaced as a prominent tool for soothsaying structure energy consumption. Through analysis of literal data, it's apparent that the relationship between lighting energy consumption and its impacting factors is non-linear.